Including smart product recommendations as part of your user experience can increase the average order value of your conversions. Here's how to get started.
A timely product recommendation can lead shoppers to choose one product over another. Look to your own experience for proof.
Have you ever made a selection at a local store, based on a product recommendation from the owner or a sales clerk? Has a product recommendation from a friend or family member ever been the deciding factor in your choice of which product to purchase?
Celebrity endorsements (paid or unpaid), advice given by a current user, “Best 10” lists on blogs – all of these mentions, and more, leverage the power of product recommendations.A timely product recommendation can lead shoppers to choose one product over another. Click To Tweet
As an ecommerce manager, though, you don’t have to wait for someone else to recommend a product to your customers. You can make product recommendations while the prospect is in the process of shopping on your website. This in-turn can lead to a better online experience and drive increased conversions.
You can even personalize those suggestions and control the conditions for their display to provide impact at the perfect time on the path to purchase.
In this article, we’ll reveal the some of the most effective ways we’ve found to deliver product recommendations. The information we’ll provide here can help you sell more products more often.
How Do Ecommerce Product Recommendation Engines Work?
While it’s possible to manually implement rudimentary “also-liked” recommendations on your ecommerce site, product recommendations best practices call for the deployment of a ‘product recommendations engine’.
There are three basic approaches used to configure the underlying algorithm:
- The content-based filtering method collects data about the likes and dislikes of each visitor (cookies allow tracking over multiple visits), then makes recommendations based on historical choices by that user.
- The collaborative-filtering method incorporates data from users who have made similar choices, then combines that information to make decisions about recommendations.
- A hybrid method combines the content-based and collaborative-based methods to incorporate group decisions, but focus the output based on attributes of a specific visitor.
All three methods use machine-learning algorithms to fuel the process. While the mathematical principles behind each are elaborate and complicated, the application to your site doesn’t have to be overwhelming.
What Are the Benefits of a Product Recommendations Engine?
Is the product recommendations process really worth the trouble? Isn’t the incorporation of machine learning a bit beyond the scope of all but the largest ecommerce websites?
Those are the types of questions we often hear from clients. There are times when it seems the high-tech movement is going too far, and machine-learning algorithms are a prime example of that complaint.
Given the potential benefits, though, the argument often settles itself. When a tool proves itself sufficiently valuable, the question moves from “Why?” to “How?”.
- One study estimated that almost a third of ecommerce sales during the quarter studied resulted from product recommendations
The conversion rate for visitors clicking on product recommendations was found to be 5.5x higher than for visitors who didn’t click
- A Gartner study predicts engines that gauge and react to customer intent will be capable of boosting ecommerce profits as much as 15 percent by 2020
- As online shoppers become more used to personalization, they equate it with professionalism – meaning your site needs to bump up to keep up
- An Accenture report says personalization increases the likelihood of a prospect purchasing from you by 75 percent
- 35 percent of sales on Amazon.com and 75 percent of Netflix sales were directly related to product recommendations (from these stats)
Resource: Item-to-Item Collaborative Filtering (Amazon)
Studies increasingly show the value of product recommendations and the critical role they play in personalization strategies. Recommendations not only lift conversion rates, they help deliver improved user experience to keep visitors coming back and can boost the average order value.Recommendations not only lift conversion rates, they help deliver improved user experience to keep visitors coming back. Click To Tweet
Once an ecommerce manager is convinced of the benefits of a product recommendation engine, the next step is to determine product recommendation best practices and configure the product recommendation algorithm accordingly.
Let’s move on to The Good’s list of product recommendation tips.
21 Best Practice Tips for Ecommerce Product Recommendations – The List
Your ecommerce site will lend itself to some of the following tips, but not to others. We’ll list the kinds of tactics we’ve seen our clients effectively implement. You choose the ones that seem most applicable to your own business.
- Displaying a list of suggested products based on the visitor’s browsing history (“Recommended for you”) is an often-used and effective type of product recommendation – to add deeper impact, personalize with the shopper’s name.
- Use “Frequently bought together” recommendations to increase average order value (AOV).
- “Featured recommendation” and “Recently viewed” suggestions can introduce shoppers to items they wouldn’t have thought about searching for.
- Providing access to the shopper’s browsing history can help save sales that may have been lost had the customer not been able to relocate an item earlier viewed.
- Show “Related to items you’ve viewed” suggestions to help with ideas.
- “Customers who bought [this item] also bought [that item]” recommendations provide social proof and peer-generated recommendations.
- Alert viewers of products that have been updated by generating “There is a newer version of this item” notices.
- Personalize recommendations by showing items related to previous purchases (“Since you already own this, you may also want this”).
- Feature best-selling items for each brand for indirect social proof and as a way of adding confidence to the purchase.
- Back up your online product recommendations with emails. Encourage shoppers to come back to purchase a recommended product or deliver additional suggestions.
- Generate product bundles (items frequently purchased together) and offer a special discount for purchasing the group.
- Don’t limit references to best-selling items to one product or brand – show best-sellers across entire product categories.
- Make sure all recommendations are relevant and timely – they should also be informed by returns and reviews.
- Adjust your recommendations to keep best-sellers highlighted AND to provide additional viewing opportunities for lower-selling items (20 percent of your items will provide 80 percent of your sales).
- Show highest rated items in product recommendations.
- Know your visitor: the more personalization you can add, the better your results.
- Provide product recommendations when items added to the cart require accessories (fishing reels need fishing line, flashlights need batteries, shoes often require socks).
- Use product recommendations for moving the buyer up to a more fully-featured version of the one currently being browsed (upsell).
- Use product recommendations to remind the shopper about upcoming holidays or other special events.
- Provide a “What customers ultimately buy” recommendation.
- Never stop testing: your product recommendations engine isn’t a set it and forget it function; add it to your testing regimen for conversion optimization.
The #1 Reason Why You Should Get Started with Product Recommendations
Strategic marketing plans vary from company to company. Tactics that fit one business often wouldn’t be a wise move for another business. Implementing a product recommendation engine, however, is something every ecommerce manager should seriously consider.
Here’s why: your competitors will soon enough. And the advantage gained from applying product recommendation examples, like the ones given above, is significant.
If you have questions, ask us in the comments below. We’d love to help.